Method for prescribing site-specific fertilizer application in agricultural fields

ABSTRACT

A map of site-specific amounts of a soil nutrient, to be applied in fertilizer to an agricultural field is created using a map of site-specific amounts of the soil nutrient needed to produce the maximum possible yield at the particular site. Subtracted from the site-specific amounts of nutrient needed are site-specific amounts of the nutrient currently existing in the field, thus producing the map of site-specific nutrient amounts to be added. The nutrient amounts may be added to the soil using the map and conventional variable-rate fertilizer application methods. In one embodiment, the amounts of the soil nutrient needed to produce the maximum possible yield at each site is created using a map of site-specific measures of biomass produced by the field in a past growing season or seasons, which in turn is created from a remotely sensed biomass image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 11/085,589filed Mar. 21, 2005, now U.S. Pat. No. 7,171,912 which in turn is acontinuation of application Ser. No. 10/083,681 filed Feb. 26, 2002, nowU.S. Pat. No. 6,889,620 which claims the benefit of U.S. ProvisionalApplication No. 60/272,158 filed Feb. 28, 2001.

TECHNICAL FIELD

This invention relates to a method/process that prescribes thesite-specific application of fertilizers in agricultural fields, andmore particularly to minimizing the amount of fertilizer that is appliedwhile maintaining the maximum possible yield for the field.

BACKGROUND

Fertilizers supply nutrients to the soil needed to produce variouscrops. The most common nutrients in soil are nitrogen, phosphorous andpotassium. In addition, some crops require micronutrients, such as zinc(Zn) and iron (Fe), depending on soils. Fertilizer of course costsmoney, but the risk of lower yields resulting from under-fertilizing hasin the past generally outweighed the monetary cost of over-fertilizing.Because the production of nitrogen supplying fertilizers typicallyrequires natural gas, the volatility in natural gas pricing can causenitrogen fertilizer prices to escalate unpredictably. In addition, theoveruse of fertilizer creates the potential for negative environmentalconsequences, and so from an environmental perspective too it isdesirable to minimize the application of fertilizers. Some governmentsin fact, in Europe for the most part, closely regulate the amount offertilizers that farmers apply.

A conventional method for calculating nitrogen needs for a fieldinvolves the following equation:N _(FERT) =N _(CROP) −N _(RES.SOIL)−(N _(OM) +N _(PREV.CROP) +N_(MANURE))−N _(IRR)

where: N_(FERT)=fertilizer N recommendation

-   -   N_(CROP)=yield goal×N yield factor    -   N_(RES.SOIL)=preplant soil profile NO₃-content (or residual        soil)    -   N_(OM)=organic N mineralization    -   N_(PREV.CROP)=legume N availability    -   N_(MANURE)=manure N availability    -   N_(IRR)=irrigation water N availability        Havlin et al., Soil Fertility and Fertilizers, 1999, Prentice        Hall, New Jersey, at pages 350-51. The equation above differs        from that provided in Havlin et al., in that the equation above        factors in the availability of N from irrigation water        (N_(IRR)). As discussed by Havlin et al. on page 351, N_(CROP)        represents the nitrogen required by the crop and involves        predicting the crop yield and the nitrogen needed to produce        that yield. A measure of “biomass,” which is basically the        density or amount of plant-life, is known to be directly related        to crop yield. One measure of biomass for crops such as corn and        soybeans is known as “leaf area index” (LAI), which can be        measured in at least two ways. First, LAI can be measured        directly by taking all the crop leaves from a unit field area        and measuring in a laboratory the total area of one side of the        leaves using an area meter. Another way by which LAI can be        derived is from remotely sensed data using a canopy reflectance        model. See Kuusk, “A Fast, Invertible Canopy Reflectance Model,”        in Remote Sensing of Environment, 51:342-50 (1995); Verhoef,        “Light Scattering by Leaf Layers with Application to Canopy        Reflectance Modeling: The SAIL Model,” in Remote Sensing of        Environment, 16:125-41 (1984). The later method is        non-destructive to the crops and suitable for agricultural field        management.

Precision farming techniques utilizing, for example, Global PositioningSystem (GPS) technology has found many uses, one being the applicationof fertilizers in agricultural fields, as is described, for example, inU.S. Pat. No. 5,220,876 to Monson et al. The '876 patent describes avariable-rate fertilizer application system. The system has a digitalmap characterizing the field's soil types. The system also has othermaps that characterize the desired level of various fertilizer types tobe applied upon the field. The patent states that the levels offertilizer can be determined from predefined characteristics, such asexisting fertilizer levels, field topography or drainage studies. Aprocessor calculates and controls the dispensing of the variousfertilizers based on both the soil map and the fertilizer map. Aposition locator on the vehicle dispensing the fertilizers provides thenecessary location information to apply the prescribed amount offertilizer in the correct location. Related U.S. Pat. No. 5,355,815 toMonson describes a closed-loop fertilizer application system, which alsovaries the application rate, but which does not require maps of currentfertilizer levels. The system is said to be able to determine a chemicalprescription in real-time for a soil scene, depending on the existingsoil fertilizer content ascertained by a real-time soil analyzer. Thesystem then dispenses fertilizer in response to the prescription.

Another variable-rate fertilizer application system is described in U.S.Pat. No. 4,630,773 to Ortlip. The '773 Patent describes a system thatapplies fertilizer according to the specific needs of each individualsoil type of soil comprising a field. The patent also describes theassembly of a digital soil map for a field to be fertilized. An aerialinfrared photograph of the field is taken. The patent states that thedifferent shades in the photograph correspond to different moisturecontents of the soil types. The photograph is digitized into an array ofpixels. Each pixel is assigned a digital value based on the shading inthe photograph, such that the value is representative of the soil typethe pixel represents. The application of fertilizer is varied accordingto the digital soil map.

The maximum possible crop yield—that is, “yield goal,” which factorsinto the calculation of N_(CROP) in the equation above—that a particularlocation in a field is able to attain may vary from location tolocation. For example, there may be a patch of gravel in a field, and nomatter how much nitrogen-based fertilizer is added to that location, theyield in that location will not increase. The gravel may cause onlyweeds to grow at that location, or may hinder the growth of anyvegetation. Gravel may be on the surface, or may be at a shallow levelbelow the surface. The gravel may not be detectable from a soil image.Other factors that may vary the yield at a particular location in afield, but which also may not be detectable from a soil image, are thesoil's fertility and its pH content. Despite this potential variation inyield throughout a single field, the prior art variable-rate fertilizerapplication systems of which the present inventors are aware all employa single yield goal measure for a field.

SUMMARY

In general, the invention is a method and system that regulates theamount of fertilizer that is applied to agricultural fields while stillmaintaining the maximum possible crop yield. In one aspect, theinvention involves the creation of a map for an agricultural field ofsite-specific amounts of a soil nutrient needed at each site to producethe maximum possible crop yield that the site is capable of producing.Subtracted from the site-specific amounts of nutrient needed for thecrop are site-specific amounts of the nutrient currently existing in thefield, thus producing a map of site-specific amounts of the soilnutrient that need to be applied to the field in a fertilizer usingconventional variable rate fertilizer application methods. The inventionhas applicability to crops such as corn, both for grain and seed, grainsorghum, soybeans, cotton, cereal grains, such as barley and wheat, andforage crops. When reference is made herein to “crop” or “crops”generally, it will be understood to encompass grasses unless the contextindicates otherwise.

In various embodiments, site-specific maximum possible crop yield, or“yield goal,” predictions may be based on site-specific measures ofbiomass, such as a conventional leaf area index (LAI) for example, thatthe field produced in a past growing season or seasons. A site-specificmeasures of biomass could be also produced from a vegetation index,which is derived from visible and near-infrared bands of satelliteimages, such as normalized difference vegetation index (NDVI) andsoil-adjusted vegetation index (SVI). Alternatively, site-specificmaximum crop yield predictions may be based on a map of site-specificmeasures of a conventional soil wetness index. The site-specificmeasures of soil wetness index may be based on topographic data for thefield or other derivative methods, such as deriving from soil brightnessindex as topography surrogate layer. The methods and systems of theinvention are helpful in the prediction of various soil nutrients thatare commonly applied by fertilizer, such as nitrogen, phosphorous,potassium, organic fertilizers (manure), and micronutrients (e.g., Znand Fe). In another aspect of the invention, the systems and methodsinclude the use of a variety of methods to predict a field's organicmatter content of the soil nutrient. In one embodiment, a bare soilimage of the field acquired before planting is used to create asite-specific map of organic matter content of the nutrient in the soil,while in another embodiment, samples of the soil's electricalconductivity are used to create the site-specific map of organic mattercontent of the nutrient in the soil.

Particular implementations of the invention will have one or more of thefollowing advantages. The amount of fertilizer applied to agriculturalfields may be reduced with no impact on crop yield, thus reducing thecost of producing crops and the potential adverse environmentalconsequences of over-fertilizing. Fertilizer will not be wasted on areasof a field that will never reach high yields, for example, areas thathave gravel that negatively impacts the yield. Also, a display of thesite-specific information used and produced by the invention to a farmermay provide the farmer with the needed confidence that a decision toreduce the amount of fertilizers in certain areas of a field will haveno negative impact on yield, thereby making it more likely that theproducers will in fact reduce the amount of fertilizers to those areas.In addition, fertilization with a constant flat rate may result inunder-fertilizing some highly productive areas in a given field.Increasing the rate of fertilizer to highly productive areas can resultin increased overall yields while not substantially increasing the totalamount of fertilizer applied to the total land area. Some embodiments ofthe invention avoid the use of costly and time-consuming soil samplings,such as are employed by prior art methods, although it will berecognized that some embodiments of the present invention still utilizesoil samplings.

Some embodiments of the invention may be used to affect crop quality aswell as yield. For instance, in hard red, (bread) wheat, a minimumprotein level must be achieved to meet grading standards. Insufficientsoil nitrogen causes protein levels to be sub-standard; the method maybe used to apply additional nitrogen to areas that are detected to bedeficient. Conversely, high protein in malting barley is a deleterioustrait. It is desirable to apply sufficient nitrogen to ensure adequateyields, yet just enough such that the nitrogen is “used up” by the cropand excess is not available to be converted to protein. The method canbe used to minimize the amount of nitrogen to field areas that exhibithigh levels of residual nitrogen. The details of one or more embodimentsof the invention are set forth in the accompanying drawings and thedescription below. Other features, objects, and advantages of theinvention will be apparent from the description and drawings, and fromthe claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a computer system containing in memory acomputer program in accordance with the invention.

FIG. 2 is a conceptual diagram illustrating the operation of a computerprogram in accordance with the invention.

FIGS. 3-5 are flowcharts of steps performed by a computer programoperating in accordance with the invention.

FIG. 6 is a conceptual diagram illustrating an alternative embodiment ofthe invention.

FIG. 7 is a graph illustrating a calculation used in the FIG. 6embodiment.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

As shown in FIG. 1, a conventional computer system 100 has at least aprocessor component 102 capable of executing computer programinstructions, a memory component 104 for storing programs and data, auser interface device or devices 106 by which a person—a user— mayinteract with programs running on the system 100, and an output deviceor devices 110 such as a printer or a video monitor. These and othercomponents are connected to interact with each other by one or morebuses 108. The memory component 104 will generally include a volatilerandom access memory (RAM), a non-volatile read-only memory (ROM), andone or more large disk drives for data and programs storage.

Residing in memory 104 is a software program in accordance with theinvention. As is conventional, the software program has programinstructions 112 that may be executed to operate on data from variousimage files 114 and a database 116 of information pertaining to one ormore agricultural fields. The image files 114 may include, in oneembodiment of the invention, a satellite image of growing crops taken ata time when the biomass of the crops are at their peak and anothersatellite image of bare soil taken some time before planting. Theseimages will be discussed in more detail later. The database 116 mayinclude the following information for each agricultural field:geographic boundaries for the field; crops planted in the field fromseason-to-season, for example, corn, soybeans, etc.; yield data forthese crops; organic matter samplings that have been taken in the field;topographic data, which may be in the form of an elevation map;irrigation data that may include an amount of water irrigated for thecrop season; crop management practices; historical weather data; soilnitrate test results; manure applications; and a map of nutrient amountsthat need to be added by fertilizer. The map of nutrient amounts thatneed to be added by fertilizer shown in database 116 deserves to behighlighted at this point, as it is the data that are produced by thecomputer program of the present invention. This map can be printed outin graphic format, or it can be used to create a site-specificfertilizer prescription for the field. The site-specific fertilizerprescription can then be downloaded, for example, and used in aconventional manner to control the rate that fertilizer is applied in afield. The information contained in database 116 will be discussed inmore detail later, as that information is discussed in connection with adetailed discussion of the steps in the process of determiningsite-specific nutrient amounts to be applied to a field divided intomultiple sites.

As shown in FIG. 2, conceptually the operation of a computer program inaccordance with an embodiment of the invention. The specific example inFIG. 2 pertains to nitrogen, but the general concept of the inventionwill readily be seen to apply to other soil nutrients, such asphosphorous, potassium, organic fertilizers (e.g., manure), andmicronutrients (e.g., Zn and Fe). Two remotely sensed images, forexample, are used by the program in this embodiment of the invention.The first is an image of vegetation 120 for the field. This image isacquired, for example, when a previous crop is at or near its biomasspeak. The second image is a bare soil image 122 for the field. Thisimage is acquired, for example, prior to planting. From the raw image ofvegetation 120, the computer program creates a map of biomass measuresfor the field 124. The measures for biomass may be, as an example,measures of leaf area index (LAI), using a conventional canopyreflectance model. Other measures of biomass could include a vegetationindex derived from visible and near-infrared bands of images at peakbiomass, such as NDVI and SVI. The map of biomass measures for the field124, and using also a single-value measure for the average historicalyield for the field 126, the computer program creates a map ofsite-specific yield goal (Y_(G)) measures for the field 128. Asmentioned previously, biomass has a direct relationship to yieldpotential, and so in accordance with the invention, a map ofsite-specific yield goal measures for the field 128 is created and usedin the calculation the fertilizer that is needed to be applied to thefield. Then, from the map of yield goal measures for the field 128, andusing also the crop type to be planted 130 given that different cropshave different nutrient requirements, the computer program calculates amap of total nutrient amounts required for the field 132 (denotedN_(CROP) using an example of nitrogen being the nutrient) for theparticular crop to achieve the site-specific yield goals. As such, themap of total nutrient amounts required for the field 132 represents theamounts of nutrient needed to achieve the maximum possible yield thatany particular location is capable of sustaining.

The lower half of FIG. 2 generally depicts the calculations needed todetermine the amount of nutrient, in this case nitrogen, that alreadyexists in the soil, or in other words, the nutrient credits. Thenutrient credits—for example, N_(OM), N_(PREV.CROP), N_(IRR),N_(RES.SOIL), and N_(MANURE)—are subtracted from the total amount ofnutrient required for the crop (N_(CROP)). In more detail, from the bareraw image of bare soil 122, the computer program creates a map of soilbrightness measures for the field 134, using conventional models such ascanopy reflectance. Soil brightness is related to organic matter contentin the field, and in the case of nitrogen, the organic matter creditN_(OM). As such, the map of soil brightness measures 134 enters into thecalculation of a map of nutrient amounts existing in the field 136. Inaddition, biomass of a previous year's crop is related to the nutrientcredit for that crop (N_(PREV.CROP)), and so the map of biomass measuresfor the field 124 also enters into the calculation of the map ofnutrient amounts existing in the field 136. Residual soil nutrients, forexample, nitrogen (N_(RES.SOIL)) from previous fertilizer applicationare related to soil organic matter that is derived from soil brightnessfor the field 136. Other nutrient credit data 138, such as informationpertaining to irrigation and manure application, also enter into the mapof nutrient amounts existing in the field 136.

The map of nutrient credits 136 are subtracted from the map of totalnutrient amounts required 132 to produce a map of nutrient amounts thatneed to be added to the field 138. As discussed above, the map 138 canbe printed out in graphic form on printer 140. The map 138 can be used,as depicted by block 142, to calculate yet another map of the amounts ofa particular nitrogen-based fertilizer that needs to be added to thefield, which in turn would be used to control the application of thefertilizer in a conventional location-based application method.

FIGS. 3-5 are flowcharts that show an example of processing steps thatcould be used to implement the embodiment of the invention shown in FIG.2. It will be understood that many of the steps in the flowchart neednot be in the order depicted, while others will need to be in the orderindicated because the step is predicated on data calculated in a priorstep. The process begins with image acquisition 200 and 202. The timewindow for the maximum vegetation, or biomass, image acquisition 200 isduring a crop's last vegetative state, which for example in the U.S.cornbelt is from mid-July to mid-August. The biomass image 200 may be ofthe last crop season or any of the last five crop seasons with favorableweather conditions when the image was acquired. The biomass image ispreferably a multi-spectral image, including green, red and infraredchannels. The time window for the bare crop image acquisition 202 isduring the pre-planting stage, which in the U.S. cornbelt is from Aprilto early May. The bare soil image may be either a panchromatic (blackand white) or a multi-spectral image.

As mentioned above, the images are, for example, satellite imagesacquired by a commercial satellite such as SPOT 1, 2, 4, Landsat TM 5and 7, or IRS 5 meters. Satellite acquired multi-spectral images willpreferably have 30-meter or less spatial resolution. Panchromatic imagesgenerally have better resolution on the existing commercial satellites,and so preferably these images will be acquired with 20-meter or lessspecial resolution either from satellite or airborne platforms. It ispossible to obtain images from one of a number of commercial vendors,who may need to acquire a particular image that is requested of acustomer, or the vendor may already have acquired a requested image. Onevendor, for example, is SPOT Image Corporation (1897 Preston WhiteDrive, Reston, Va. 20191-4368, United States), who provides images incompact disc (CD) format. The image typically covers a 120 by 120square-mile area for Landsat, and 36 by 36 square-mile area for SPOT.Multi-spectral images of the above mentioned satellites typically havean individual pixel size of 30 by 30 square meters, whereas panchromaticimages typically have a pixel size of 10 by 10 square meters. Thesoftware program may require geo-referencing of the image to aparticular coordinate or globally map-projected reference system, suchas Universal Transverse Mercator (UTM) with WGS 84 datum within onepixel error. In addition, the images may be accompanied by a scenesensor parameter file that includes absolute calibration values, solarangle, satellite view angle, and relative azimuth angle for each bandand each scene of images, which is used, at block 204, to make anatmospheric correction to the images and, at block 206, to calculateleaf area index (LAI) for biomass measures. The atmospheric correctionmay be done using a relatively simple atmospheric correction algorithmto calculate canopy reflectance to eliminate haze, water vapor, ozone,aerosol, etc. See Kaufmann et al., “Algorithm for automatic atmosphericcorrections to visible and near-IR satellite imagery,” Int'l Jnl. ofRemote Sensing, 9:1357-1381 (1988); Richter, “A fast atmosphericcorrection algorithm applied to Landsat TM images,” Int'I Jnl. of RemoteSensing, 11: 159-166 (1990); Richter, “Correction of atmospheric andtopographic effects for high spatial resolution satellite imagery,” In'lJnl. of Remote Sensing, 18:1099-1111 (1997).

Next at block 206, a map of leaf area index (LAI) is calculated from thecorrected image using a conventional canopy reflectance or other similarmodel, for example, as described in Verhoef, 1985 and Kuusk, 1995. Sucha model for calculating LAI in this embodiment is also coupled with aninversion procedure that includes a search algorithm to achieve theinversion procedure. In the inversion procedure, a search algorithmsearches a look-up table database created by the canopy reflectancemodel for LAI and other model parameters to “match up” each pixel with aminimum error. The use of such a search algorithm enables thecalculation of LAI in an excellent speed. In addition, a vegetationindex, such as NDVI and SVI, can also be derived from visible andnear-infrared bands of images to infer the amount of biomass.Information 208 from database 116 (FIG. 1) about the crop types thatwere in the fields when the maximum biomass image was acquired, as wellthe geographic boundaries for the fields included in an image, are bothincluded as inputs in the calculation of the LAI map. An alternativeprocedure can be accompanied by using the maximum biomass image toobtain crop information using an image classification method. By way ofexample, an acquired image may include an area of 120 by 120 squaremiles, and thus include many agricultural fields included in database116 (FIG. 1). Also, different fields may have different crops planted atthe time of image acquisition. Therefore, the processing at block 206takes this field boundary information and the crop information from eachfield and uses that to calculate the LAI map for the field.

At block 210 a map of soil brightness index is calculated. Generally,the soil brightness index may be a relatively simple calculationinvolving the normalization of the bare soil image (either panchromaticor multi-spectral image) into a certain range of numbers. In addition,the soil brightness index can be developed with a soil reflectance modelor “soil line” methods. The “soil line” method is to use visible andnear infrared bands with a rotation of the multi-spectral data from theorigin. At 212, the maps of LAI and soil brightness index measures arecut into individual image files for the agricultural fields in database116 (FIG. 1). To do this field boundary information 214 is again needed.Then at 216 and 218 the LAI and soil brightness index measures arenormalized into ranges between 0 and 1 within a field boundary, andvalues of the measures that fall outside a calculated standard deviationare corrected.

Referring to the same flow diagram but now on FIG. 4, at 220 a map ofsite-specific yield goal (Y_(G)) measures for a particular field arecalculated, based on the map of LAI biomass measures for the field 222,the particular crop to be planted in the field 224, and historical yieldresults for past crops produced in the field 226. The historical yieldmeasure 225 in this embodiment is a single measure for the entire field,though the single measure may be the average yield over the last fivecrop years. The yield goal distribution across a given field willtypically be correlated to LAI, as yield goal has the same spatialdistribution pattern as LAI across the field. The distribution of yieldgoal measures may correspond to at least 90% of the total distributionof the corresponding LAI measures, with each end of the distributiongetting 5% cut off. This is done to minimize a skewed distribution andinaccurate measures resulting from an inaccurate field boundary. Thelower limits of a given field's yield goal (Y_(G)), for example, may beset to be 50 bushels/acre for a location. This is done to guarantee thatthere will be enough N-based fertilizer for a low elevation spot wherethere is typically waterlogging in the field in the event that theupcoming growing seasons turns out to be abnormally dry.

In steps 226, 232, 238, 242 and 248 the nutrient credits are calculatedfor the agricultural field. At 226 a site-specific nitrogen (N) creditfor organic matter (N_(OM)) is calculated. Entering into calculation226, and shown by the input at 228, is the average field organic mattercontent (% of dry weight) measured in the last five-years of soilsamples. It may be assumed that the field organic matter content is notchanging in the last five years. Also entering into calculation 226 isthe map of soil brightness measures 230 for the field. Soil brightnesshas a known relationship to organic matter content, topography, and soilmoisture distribution. A lower soil brightness corresponds to highersoil organic matter content, a lower position in the field's topography,and a relatively higher soil moisture. In similar manner to calculation220, upper and lower limits may be set for the distribution ofnormalized soil brightness and average field organic matter content.

At 232 a site-specific N credit from a previous crop (N_(PREV.CROP)) iscalculated, based on the map of LAI biomass measures for the field 234and the previous growing season's crop type. The most common reason forsuch a credit is when the previous crop was a crop of soybeans or otherlegume crops. By way of example, the average N credit for a previousseason crop of soybeans may be 30 pounds/acre because soybeans fixnitrogen in the soil. The N credit for a previous soybean crop is alsorelated to the biomass of that crop, and thus the biomass measures 234enter into the calculation at 232. At 238 a flat-rate N credit forhaving irrigated water (N_(IRR)) is calculated. This requires inputs 240that may include, for example, the total amount in inches of irrigatedwater over a period of time and a number in parts-per-million (ppm) ofnitrate N in the water for the particular season and location. Theaverage N credit from irrigated may be estimated to be 2.7pound-N/acre-foot per ppm nitrate N. See Havlin, 1999.

At 238 a site-specific N credit for residual soil (N_(RES.SOIL)) iscalculated. The number in ppm of average nitrate N for the given fieldmay be calculated based on the previous crop type and managementpractices, weather conditions, and topography of the field. Thisinformation may also be obtained through soil nitrate testing donebefore fertilizer is applied. In one embodiment, N_(RES.SOIL) is basedon normalized values for soil brightness index 244 and average ppmnitrate N from soil nitrate testing 246. As discussed in connection withother steps, again there may be set upper and lower limits for thedistribution of the normalized values. By way of example, the average Ncredit may be 3.6 pound-N/ppm nitrate N. See Havlin, 1999. At 248 aflat-rate N credit for manure (N_(MANURE)) is calculated, based oninputs 250 of the amount of manure applied per acre and the type ofmanure applied (for example, from a hog farm or a chicken farm). Theaverage N credit for manure may be 10 pound-N/ton-manure, dependingagain on the type of manure. See Havlin, 1999. The N credit for manurefrom a chicken farm would typically be greater than the N credit formanure from a hog farm. Manure also may affect soil nutrients in thefollowing crop seasons depending on the condition of manuredecomposition and nutrients removed from soils from crops.

Referring to FIG. 5, at 252 a site-specific total N requirement(N_(CROP)) is calculated, based on the site-specific yield goalscalculated at 220 (FIG. 4) multiplied by an N conversion factor. Asmentioned previously, this map of NCROP represents the amount ofnitrogen needed to produce the maximum possible yield that theparticular location is able to sustain. The conversion factor may range,for example, from 1.0 to 1.4 for most U.S. cornbelt soils. The Nconversion factor preferably will be dependent on weather conditions andsoil properties. At 254 the final map of site-specific measures ofnitrogen (N_(FERT)) that needs to be added to the field is calculated bysubtracting all the N credits (N_(OM), N_(PREV.CROP), N_(IRR),N_(RES.SOIL), and N_(MANURE)) from N_(CROP). The map of N_(FERT)measures is available to be used in the manners described previously.

In an alternative embodiment, shown in FIG. 6, instead of being based ona maximum biomass image 120 as in the FIG. 2 embodiment, the map 128 ofyield goal measures for the field is created based on a map 324 of soilwetness index (WI) measures for the field, which in turn is calculatedbased on topography data 320 for the field. Previous research has showna direct relationship between the amount of water available and yieldvariability in a field. Water effects on yield are believed to be theresult of a combination of total precipitation amount and distributionof the precipitation during the season. A field sub-area with stresscausing yield reduction in a field can be caused by either lack orexcess of water. For example, a high wetness index area may be subjectedto excessive soil moisture during parts of the season causing yieldreductions by stand reductions, shallow roots and de-nitrification. Onthe other hand, a sub-field area with low wetness index may experienceyield reductions due to lack of water. The extent to which this willoccur depends on the weather that is by nature unpredictable. In a verydry year, for example, the areas of the field with high wetness indiceswill probably be the best producing area of a field, if not subjected toother factors such as pests and diseases.

The wetness index (WI) for a given point in a field may be calculatedtaking into account the total area draining to the field and the slopeat the point, using the following equation:W1=1n(As/tan α)

where: As=specific cachments area

-   -   α=slope angle        See Moore et al., “Soil Attribute Prediction Using Terrain        Analysis,” in Soil Sci. Soc. Am. Jnl., 57:443-452 (1993). The        wetness index may be considered as an indicator of the        probability of soil moisture levels during the season, the        higher the wetness index for a given sub-area of a field, the        higher the probability that the particular sub-area will        experience higher levels of moisture during the season. The        yield goal relationship shown in FIG. 7 may be shifted depending        if the field is normally subjected to drainage problems (shift        to the left) or to the right if the field is located in an        upland position with well-drained soils or soils with low        water-holding capacity. In fact, in the latter case it is        possible that the yield goal would not decrease for high wet        indices (dashed line).

Also shown in FIG. 6 is another alternative to the FIG. 2 embodiment.Instead of using a bare soil image 122 as a measure of organic matter inthe field (FIG. 2), soil electrical conductivity (EC) measurements 322made in the field are used (FIG. 6). Research has shown that soil ECpositively correlates to organic matter. Soil EC thus may be used as asurrogate measurement to organic matter and the ability of the soil tosupply N during the season.

As mentioned previously, the invention applies to soil nutrients otherthan nitrogen. For example, the invention applies to phosphorous (P),potassium, micronutrients such as Zn and Fe, and organic fertilizer suchas manure. The calculations will of course differ depending on thenutrient, but the inventive aspects remain the same. To give an exampleof some of the possible differences between calculations for differentnutrients, nitrogen existing in soil is in large part depleted or washedaway each growing season (except of course, if the crop is soybeans, asnoted previously). For other nutrients, notably phosphorous, this is notthe case, and so the past management of the field with respect to theparticular nutrient will weigh much more heavily in the calculation ofthe site-specific amounts of the nutrient that need to be applied to afield. Also, with respect to phosphorous, it is known that the pH of thesoil directly relates to the amount of phosphorous in the soil. As such,an implementation of the invention to provide a prescription forphosphorous application may require soil pH samples, although there maybe other ways to estimate pH in various locations of a field. Inaddition, the phosphorous prescription may also include biomass measuresto determine the site-specific maximum possible crop yields for thefield. It will be recognized that the invention will be particularlyuseful to provide a phosphorous prescription in areas where the lack ofphosphorous in the soil is a particular problem, such as in areas ofBrazil.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

1. A method implemented in at least one computer program to provide a map of site-specific amounts of a soil nutrient to be applied in fertilizer to an agricultural field divided into sites, the method comprising: calculating for the field, by use of a map of site-specific measures of a soil wetness index, a map of site-specific amounts of the soil nutrients needed to produce at each site a maximum possible crop yield; and subtracting, from the site-specific amounts of the soil nutrient needed to produce at each site a maximum yield, site-specific measures for the soil nutrient existing in the field, thereby producing a map of site-specific amounts of the soil nutrient to be applied in fertilizer to the field.
 2. The method of claim 1, wherein the soil nutrient is nitrogen, phosphorous, or potassium.
 3. The method of claim 1, wherein the soil nutrient is organic fertilizer or manure.
 4. The method of claim 1, wherein the soil nutrient is a micronutrient.
 5. The method of claim 4, wherein the micronutrient is Zn or Fe.
 6. The method of claim 1, further comprising: calculating for the field, from a map of site-specific image data taken of the field in a bare soil state, a map of site-specific measures of soil brightness; and calculating the site-specific measures of the soil nutrient existing in the field from at least the map of site-specific measures of soil brightness.
 7. The method of claim 1, wherein the site-specific measures of the soil nutrient existing in the field are calculated from at least a map of a site-specific measure of soil electrical conductivity.
 8. A computer program, residing on a computer-readable medium, for providing a map of site-specific amounts of a soil nutrient to be applied in fertilizer to an agricultural field divided into sites, the computer program comprising: instructions for causing a computer to: calculate for the field, from a topographic map for the field, a map of site-specific measures of a soil wetness index; calculate for the field, from the map of site-specific measures of a soil wetness index, a map of site-specific amounts of the soil nutrient needed to produce at each site a maximum possible crop yield; and subtract, from the site-specific amounts of the soil nutrient needed to produce at each site a maximum possible crop yield, site-specific measures of the soil nutrient existing in the field, thereby producing a map of site-specific amounts of the soil nutrient to be applied in fertilizer to the field.
 9. The computer program of claim 8, wherein the soil nutrient is nitrogen, phosphorous, or potassium.
 10. The computer program of claim 8, wherein the soil nutrient is organic fertilizer or manure.
 11. The computer program of claim 8, wherein the soil nutrient is a micronutrient.
 12. The computer program of claim 11, wherein the micronutrient is Zn or Fe.
 13. The computer program of claim 8, wherein the instructions further cause the computer to: calculate for the field, from a map of site-specific image data taken of the field in a bare soil state, a map of site-specific measures of soil brightness; and calculate the site-specific measures of the soil nutrient existing in the field from at least the map of site-specific measures of soil brightness.
 14. The computer program of claim 8, wherein the instructions cause the site-specific measures of the soil nutrient existing in the field to be calculated from at least a map of site-specific measure of soil electrical conductivity.
 15. A method implemented in at least one computer program to provide a map of site-specific amounts of a soil nutrient to be applied in fertilizer to an agricultural field divided into sites, the method comprising: calculating a map for the agricultural field of site-specific of soil nutrient amounts needed to produce at each site a maximum possible crop yield by use of a map of site-specific field characteristic data for the agricultural field generated using a satellite image of the agricultural field; and subtracting, from the site-specific soil nutrient amounts for maximum yield for the field, site-specific measures for the soil nutrient existing in the field, thereby producing a map of site-specific amounts of the soil nutrient to be applied in fertilizer to the field.
 16. The method of claim 15, wherein the field characteristic is a measure of biomass produced by the field in one or more past growing seasons.
 17. The method of claim 15, further comprising: calculating, from a map of site-specific image data taken of the field during one or more past growing seasons, a map of site-specific measures of a leaf area index, the leaf area index serving as the measure of biomass produced by the field.
 18. The method of claim 15, further comprising: calculating, from a map of site-specific image data taken of the field during one or more past growing seasons, a map of site-specific measures of a vegetation index, the vegetation index serving as the measure of biomass produced by the field.
 19. The method of claim 15, further comprising: calculating, from a topographic map for the field, a map of site-specific measures of a soil wetness index, the wetness index serving as the field characteristic.
 20. The method of claim 15, wherein the soil nutrient is nitrogen, phosphorous, or potassium.
 21. The method of claim 15, wherein the soil nutrient is organic fertilizer or manure.
 22. The method of claim 15, wherein the soil nutrient is a micronutrient.
 23. The method of claim 22, wherein the micronutrient is Zn or Fe.
 24. The method of claim 15, further comprising: calculating for the field, from a map of site-specific image data taken of the field in a bare soil state, a map of site-specific measures of soil brightness; and calculating the site-specific measures of the soil nutrient existing in the field from at least the map of site-specific measures of soil brightness.
 25. The method of claim 15, wherein the site-specific measures of the soil nutrient existing in the field are calculated from at least a map of site-specific measure of soil electrical conductivity. 